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Long Range Forecasting: Spring 2004

Long Range Forecasting: Spring 2004. Paul Knight & Rich Grumm PSU/NWS Univ Park/State College PA. Introduction. The Great Tragedy of Science - the slaying of a beautiful hypothesis by an ugly fact T.H. Huxley (1825-95) Define Terms Overview of the Material Developing the Discipline.

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Long Range Forecasting: Spring 2004

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  1. Long Range Forecasting:Spring 2004 Paul Knight & Rich Grumm PSU/NWS Univ Park/State College PA

  2. Introduction • The Great Tragedy of Science - the slaying of a beautiful hypothesis by an ugly fact • T.H. Huxley (1825-95) • Define Terms • Overview of the Material • Developing the Discipline

  3. Defining Terms • Extended Forecasting • a prediction of weather conditions for a period extending beyond more than 3 days from issuance • Medium Range Forecasting • a prediction of weather conditions for a period extending from 3 to 7 days from issuance • Long Range Forecasting • a prediction of weather conditions for a period extending beyond more than 7 days from issuance - there is no limit how far beyond

  4. AMS Statement on Extended Forecasting • 1991 • 6 to 10 days: some skill in mean temp and precip relative to climatology (T better than P) • Monthly/Seasonal forecasts: slight skill in mean temperature and precipitation departure; but no skill in day to day forecasts • 2001 • dramatic improvement in 1-2 seasons in advance forecasts of temp and precip • still no day-to-day skill beyond 1-2 weeks

  5. Long Range Forecasting Class

  6. Long Range Forecasting Class

  7. Long Range Forecasting Class

  8. Long Range Forecasting Class

  9. Long Range Forecasting Class

  10. Long Range Forecasting Class

  11. Long Range Forecasting Class

  12. Long Range Forecasting Class

  13. Long Range Forecasting Class

  14. Long Range Forecasting Class

  15. Long Range Forecasting Class

  16. Secrets Revealed • The Tropics (oceans) drive the changes in seasonal and annual conditions in the middle latitudes • The myth of the perfect analog • While an enormous number of cases are needed for ‘the perfect analog’, a substantial amount of useful information is available from a carefully selected few. • The start of desktop LRF research (for regional/local connections)

  17. Developing the Discipline • Recent Past • What has been the trend? • Why has the trend changed? • Current conditions • What are the most salient features? • Why is it happening? • Forecast conditions • What do the dynamic and statistical models show and why?

  18. Past Conditions Fall (Sept-Nov) 2003 Temperatures

  19. Past Conditions 500 hPa anomalies for last 35 days

  20. Past Conditions

  21. Current Conditions

  22. Current Conditions

  23. Future Conditions

  24. Future Conditions

  25. The Master Forecaster • Seeks to learn what is going on • Diagnosis leads to understanding • Use remotely sensed and model data • Seeks to understand what will happen • Medium range analysis tools integrating data sets • Model tools to forecast at long range

  26. Forecast Funnel Theory(traditional view) • represents the scales of interaction: hemispheric, synoptic, mesoscale and local that influence the onset of and changes in weather events for a particular forecast area. These scale interactions establish a context for demonstrating and establishing essential forecasting skills. • Forecasters spend more time on details near bottom of the funnel the local scale

  27. COMET’s Funnel-Pyramid

  28. (Updated) Forecast Funnel • Consider forecast length (time) as well as scale • Consider tools for both the scale and the time • Forecaster time will be focused more on the details as the weather gets more interesting • Sensible weather is local

  29. Satellites S C A L E Ensembles Climate (PNA/NAO) Climatic Anomalies Satellites Analysis diagnosis Ensembles Models and Climatic Anomalies Mesoscale Models and ensembles Mesoscale Models Radar Analysis Weeks Days Hours Event Time Scales of Prediction

  30. References Buroughs, W.J., 1992: Weather Cycles: Real or Imaginary? Cambridge University Press. ISBN 0 521 47869 3 Brooks, H.E, C. A. Doswell III, and R.A. Maddox, 1993: On the Use of Mesoscale and Cloud-Scale Models in Operational Forecasting. Wea. Fore.7, 7, 120-132. URL’s: http://grads.iges.org/ellfb/Dec02/Pierce/fig1.gif http://www.cdc.noaa.gov/HistData/ http://opwx.db.erau.edu/~herbster/wx427/fcst_process.html http://meted.ucar.edu/mesoprim/mesodefn/print.htm http://www.atmos.colostate.edu/ao/Figures/Thompson_Wallace_Science2001/index.html http://www.cpc.ncep.noaa.gov/products/tanal/accesspage.html http://ingrid.ldeo.columbia.edu/maproom/Global http://www.rap.ucar.edu/weather/surface/us_AFsnow.gif

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